Following previous settings [1]}, [2]}, we assume to have access to \(M\)  candidate objects in the scene \(\rm {S}\)  under a specific view. Objects are either generated from the ground truth as in Referit3D [1]} or predicted by a 3D object detector as in ScanRefer [2]}.
3D object encoding extracts features from 3D point cloud data, which is the main computing bottleneck, and it is more costly to encode features for multiple views. Therefore, we decoupled the 3D object feature extraction process into two steps, i.e., computing point clouds features and multi-view positional encoding. This decoupling can significantly save the computational cost since different views can share the same point cloud features outputted from the first step. Finally, the object features can be calculated by simply combining both.
